AI Data Solutions
Data engineering for AI: pipelines, warehouses, feature stores, vector indexes and governance that make AI systems accurate.
- Understand requestIntent: refund · order #4821
- Retrieve policyRefund policy v3 · 2 sources cited
- Call toolsorders.lookup · payments.status
- 4Human approvalRefund above auto-approve limit
- 5Execute actionpayments.refund
- 6Respond & logCustomer reply · audit trail
Tool call
orders.lookup({
order_id: "4821"
}) → { status: "delivered",
amount: 42.00 }Approval required
Refund 42.00 to original payment method
Division
Service area
Machine Learning, NLP & Computer Vision
Engagement
Project · Team · Managed
Overview
AI systems are only as good as the data behind them. We build the data foundations AI needs: ingestion pipelines from operational systems, cleaned and modelled warehouse layers, feature stores for machine learning, document and vector indexes for retrieval, and the governance — lineage, quality checks, access control — that lets teams trust the data. This work often unlocks several AI use cases at once.
Common use cases
- AI-ready data platformCentral, governed data for analytics, ML and RAG.
- Feature storeConsistent features for training and real-time inference.
- Knowledge indexingDocument pipelines feeding vector and keyword indexes.
- Data quality programmeAutomated tests and monitoring for critical datasets.
Quick answers
AI Data Solutions at a glance
The essentials in brief. Every project is scoped individually — ask us for specifics.
- What is AI data solutions?
- Data engineering for AI: pipelines, warehouses, feature stores, vector indexes and governance that make AI systems accurate.
- Who is it for?
- Typically product companies adding AI features, enterprises automating knowledge work, and teams whose AI pilot needs to become a dependable production system.
- What does Shivacha provide?
- Ingestion pipelines
- Data modelling
- Feature engineering platform
- Unstructured data pipelines
- Data governance
- Quality monitoring
- Which technologies are used?
- Apache Airflow, dbt, Snowflake, BigQuery, Apache Spark, Vector Databases — chosen to fit your stack and constraints.
- How does the process work?
- Frame the prediction → Audit the data → Baseline then improve → Deploy with MLOps → Monitor and retrain.
- What affects the cost?
- Number and quality of data sources
- Accuracy targets and evaluation effort
- Integrations with business systems
- Model choice, hosting and per-request cost
- Human-in-the-loop and audit requirements
- Data residency and privacy constraints
- How long does it take?
- A scoped proof of value usually takes 4–8 weeks; a production system with evaluation, integrations and guardrails typically takes 3–6 months.
- How do I get started?
- Share a short brief in the form below, book a 30-minute call or message us on WhatsApp. A senior engineer replies within one business day; NDA on request.
Capabilities
What we deliver
Ingestion pipelines
Batch and streaming connectors from databases, SaaS and files.
Data modelling
Warehouse layers designed for analytics and ML.
Feature engineering platform
Reusable, versioned features.
Unstructured data pipelines
Parsing, chunking and embedding of documents.
Data governance
Lineage, catalogues, access policies and PII handling.
Quality monitoring
Freshness, completeness and anomaly checks.
Architecture
Engineered right from day one
The layers we typically design for machine learning, NLP & computer vision, adapted to your stack and partners.
- ExplainabilityFeature importance and reason codes where decisions affect customers.
- Data leakageRigorous validation splits so offline accuracy reflects production reality.
- Latency budgetsModel size and serving architecture matched to real-time requirements.
- FairnessBias testing across relevant segments for decisions with customer impact.
Delivery
How an engagement runs
- 1
Frame the prediction
Define the target, decision it supports, acceptable error and business metric.
- 2
Audit the data
Assess availability, quality, leakage and labeling needs before modeling.
- 3
Baseline then improve
Start with simple, explainable baselines and add complexity only when it pays.
- 4
Deploy with MLOps
Versioned models, reproducible pipelines and automated deployment.
- 5
Monitor and retrain
Track drift and performance in production and retrain on a schedule or trigger.
Security
Security built into delivery
Controls we apply by default on this kind of work — not a separate phase at the end.
Data boundaries
Permission-aware retrieval so users only see answers from documents they may access.
Guardrails
Input and output checks, tool allow-lists and human approval for consequential actions.
No training on your data
Provider settings and contracts chosen so your data is not used to train third-party models.
Audit trail
Prompts, sources, tool calls and approvals logged for review.
Technology
Tools we use for this
Related services
Often combined with
Machine Learning Development
Custom machine learning models for prediction, scoring, forecasting and recommendation, deployed and monitored with MLOps.
Learn moreNLP Development
Natural language processing for classification, entity extraction, sentiment, search and multilingual text understanding.
Learn moreComputer Vision Development
Computer vision systems for inspection, detection, recognition and visual analytics — from cloud APIs to edge devices.
Learn moreDedicated team
Machine Learning Team
ML engineers and data scientists for predictive models and MLOps.
Work & insights
Related thinking
Agentic claims intake with human approval
A reference design for an AI workflow that reads claim submissions, extracts and validates data, and prepares cases for adjusters.
Learn morePermission-aware enterprise knowledge assistant
How we design a RAG assistant that answers from thousands of internal documents while respecting every user's access rights.
Learn moreAI development cost: what you pay for when you build an AI product or agent
Model fees are rarely the main cost. Data preparation, evaluation, integrations and guardrails decide both the budget and whether the system works.
Learn moreFAQ
Frequently asked questions
Do we need a data warehouse before doing AI?
Not always — many LLM use cases work directly from document sources. But ML models and analytics-grounded assistants benefit greatly from a modelled warehouse.
Which data platforms do you work with?
Snowflake, BigQuery, Databricks-style lakehouses, PostgreSQL and ClickHouse, with orchestration using tools such as Airflow and dbt.
How much data do we need?
It depends on the problem. Some tasks work with a few thousand labeled examples, especially with pre-trained models; others need much more. A short data audit gives a reliable answer before significant investment.
Can models run on devices or at the edge?
Yes. We optimise and quantise models for mobile, embedded and edge hardware where latency, connectivity or privacy require local inference.
Next step
Build Your AI Product.
Tell us about your AI data solutions requirements — goals, timeline and constraints. We will reply with questions, an approach and next steps.
- Senior engineer reads every enquiry
- Reply within one business day
- NDA on request
Your details are used only to reply to this enquiry.